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Decision Trees, Survival Trees, and Random Forest: Practical Examples with R Programming

In this session of the BTEP Coding Club, Brian Luke, PhD, Senior Principal Computational Scientist with the Advanced Biomedical Computational Science (ABCS) group, demonstrated the use of R programming to perform decision tree analysis, survival tree analysis, and random forest.

The session covered the following:

  1. Decision Tree Analysis

    The decision tree analysis used the “kyphosis” dataset to predict the absence or presence of kyphosis (a type of deformation) following corrective spinal surgery.

  2. Survival Tree Analysis

    The survival tree analysis used the recurrence-free survival time from a prospective randomized clinical trial conducted by the German Breast Cancer Study Group.

  3. Random Forest

    Random forest was applied to the German Credit Data set to determine whether they should or should not receive a loan of a given amount.

R script

Access the R script used in this tutorial here.

For a more detailed theoretical background on these topics, check out this related presentation ("Decision Trees, Survival Trees, and Random Forest") also by Brian Luke, Ph.D.